Factors and mechanisms driving among-lake variability of mercury concentrations in a benthivorous fish in the canadian subarctic
Bibliographic record
Abstract
Wild-caught fish are an important subsistence food source in remote northern regions, but they can also be a source of exposure to mercury (Hg), which has known health hazards. We investigated factors and mechanisms that control variability of Hg concentrations in Lake Whitefish (Coregonus clupeaformis) among remote subarctic lakes in Northwest Territories, Canada. Integrating variables that reflect fish ecology, in-lake conditions, and catchment attributes, we aimed to not only determine factors that best explain among-lake variability of fish Hg, but also to provide a whole-ecosystem understanding of interactions that drive among-lake variability of fish Hg. Size-standardized concentrations of total Hg ([THg]) in Lake Whitefish varied threefold (0.05-0.15 mg/kg wet weight) and differed significantly among the twelve study lakes. Stepwise multiple regressions revealed that 84% of among-lake variability in size-standardized fish [THg] was explained by positive relationships with two variables, catchment to lake area ratios (CA:LA) and methyl Hg concentrations ([MeHg]) in benthic invertebrates. Piecewise structural equation modeling indicated that [MeHg] in benthic invertebrates were positively related to [THg] in sediment and [MeHg] in water, which in turn were both positively related to concentrations of dissolved organic carbon (DOC) in water. Fish [THg] and all proximate in-lake drivers were ultimately driven by catchment attributes and were higher in lakes within lower-elevation, relatively larger, proportionally more forested catchments. Revealing interactive processes that influence fish Hg levels, our findings improve the current knowledge about causes of Hg variability among subarctic lakes and highlight factors that can help guide future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".